EV charging stations in suburban areas face an intrinsic trade-off between operator profitability and user affordability, an issue that is not sufficiently addressed by previous literature due to strict charging deadlines imposed on each vehicle, which tend to be overlooked. This study proposes a pricing scheme for the operation of an EV charging station in suburbia under time-of-use (ToU) electricity grid tariffs. The pricing system uses an iterative pricing engine that calculates the lowest possible price per kilowatt-hour at which the station operator can earn a predetermined target profit margin.Three optimisation models, namely Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Mixed Integer Programming (MIP), have been developed and analysed under similar experimental settings. Both GA and PSO are combined with the Earliest Deadline First (EDF) scheduler, while MIP optimises scheduling decisions directly.Experimental studies have been carried out over twenty-five trials for electric vehicle(EV) populations ranging from 50 to 100. The results indicate that PSO provides themaximum profit value (Rs 565.12 when n = 100). In contrast, MIP consistently offers the minimum selling price (Rs 6.48–6.63 per kWh), along with the fastest computation time (less than 1.6 seconds). All optimisation techniques achieved the desired profit margin of up to 20%.Index Terms: Electric vehicle charging, Dynamic pricing, Genetic Algorithm, Particle Swarm Optimisation, Mixed Integer Programming, Earliest Deadline First, Profit maximization, Time-of-use tarif.
Thupakula et al. (Fri,) studied this question.